Data Assimilation of Ambient Concentrations of Multiple Air Pollutants Using an Emission-Concentration Response Modeling Framework

Xing, Jia; Li, Siwei; Ding, Dian; Kelly, James T.; Jang, Cholsoon; Zhu, Yun; Hao, Jiming

doi:10.3390/atmos11121289

Cited by 12 publications

(10 citation statements)

References 31 publications

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“…It is worth noting that the adjustment range of NO x is much lower than that of VOCs, as only the observed concentration of NO 2 is used as a constraint. Such results are consistent with our previous study (Xing et al, 2020a).…”

Section: Emission Inventory Updating and Analysissupporting

confidence: 94%

“…Recently, Yang et al (2021) linked the bottom-up China Multi-pollutant Abatement Planning and Long-term benefit Evaluation (China-MAPLE), model with the top-down computable general equilibrium (CGE) model to evaluate the comprehensive impacts of deep decarbonization pathways (DDPs) in China. However, most of the previous studies have merely focused on individual pollutants that can be measured directly (Brioude et al, 2012;Xing et al, 2020a;Yang et al, 2021) and have relied on traditional numerical modeling.…”

Section: Introductionmentioning

confidence: 99%

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Exploring deep learning for air pollutant emission estimation

et al. 2021

Self Cite

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Abstract. The inaccuracy of anthropogenic emission inventories on a high-resolution scale due to insufficient basic data is one of the major reasons for the deviation between air quality model and observation results. A bottom-up approach, which is a typical emission inventory estimation method, requires a lot of human labor and material resources, whereas a top-down approach focuses on individual pollutants that can be measured directly as well as relying heavily on traditional numerical modeling. Lately, the deep neural network approach has achieved rapid development due to its high efficiency and nonlinear expression ability. In this study, we proposed a novel method to model the dual relationship between an emission inventory and pollution concentrations for emission inventory estimation. Specifically, we utilized a neural-network-based comprehensive chemical transport model (NN-CTM) to explore the complex correlation between emission and air pollution. We further updated the emission inventory based on back-propagating the gradient of the loss function measuring the deviation between NN-CTM and observations from surface monitors. We first mimicked the CTM model with neural networks (NNs) and achieved a relatively good representation of the CTM, with similarity reaching 95 %. To reduce the gap between the CTM and observations, the NN model suggests updated emissions of NOx, NH3, SO2, volatile organic compounds (VOCs) and primary PM2.5 changing, on average, by −1.34 %, −2.65 %, −11.66 %, −19.19 % and 3.51 %, respectively, in China for 2015. Such ratios of NOx and PM2.5 are even higher (∼ 10 %) in regions that suffer from large uncertainties in original emissions, such as Northwest China. The updated emission inventory can improve model performance and make it closer to observations. The mean absolute error for NO2, SO2, O3 and PM2.5 concentrations are reduced significantly (by about 10 %–20 %), indicating the high feasibility of NN-CTM in terms of significantly improving both the accuracy of the emission inventory and the performance of the air quality model.

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Section: Emission Inventory Updating and Analysissupporting

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Exploring deep learning for air pollutant emission estimation

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…It is worth noting that the adjustment range of NOx is much lower than of VOC, because only the observed concentration of NO2 is used as a constraint. Such results are consistent with our previous study (Xing et al, 2020a).…”

Section: Emission Inventory Updating and Analysissupporting

confidence: 94%

“…Recently, Yang et al (2021) linked the bottom-up MAPLE model with the top-down CGE model to evaluate deep decarbonization pathways' (DDP) comprehensive impacts in China. However, most of previous studies merely focused on individual pollutants that can be measured directly (Brioude et al, 2012;Xing et al, 2020a;Yang et al, 2021) and relied on traditional numerical modelling.…”

Section: Introductionmentioning

confidence: 99%

“…On the contrary, neural networks (NN), as a more efficient tool, can also model complex nonlinear relations in the atmospheric system thus converting precursor emissions into ambient concentrations. Due to its ability of end-to-end learning, NN can automatically extract key features of input data and capture the behaviour of target data, thus has been widely used in atmospheric science recently (Fan et al, 2017;Tao et al, 2019;Wen et al, 2019;Xing et al, 2020a;Xing et al, 2020c). For example, many studies (Fan et al, 2017;Tao et al, 2019;Wen et al, 2019) (Cho et al, 2014;Chung et al, 2014;Hochreiter and Schmidhuber, 1997) with certain ability to handle missing values efficiently (Fan et al, 2017) and CNN exhibits potentials in leveraging spatial dependencies, e.g., in meteorological prediction (Krizhevsky et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

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Exploring Deep Learning for Air Pollutant Emission Estimation

Huang¹,

Liu²,

Yang³

et al. 2021

Preprint

Self Cite

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Abstract. The inaccuracy of anthropogenic emission inventory on a high-resolution scale due to insufficient basic data is one of the major reasons for the deviation between air quality model and observation results. A bottom-up approach, as a typical emission inventory estimation approach, requires a lot of human labor and material resources, and a top-down approach focuses on individual pollutants that can be measured directly and relies heavily on traditional numerical modelling. Lately, deep neural network has achieved rapid development due to its high efficiency and non-linear expression ability. In this study, we proposed a novel method to model the dual relationship between emission inventory and pollution concentration for emission inventory estimation. Specifically, we utilized a neural network based comprehensive chemical transport model (NN-CTM) to learn the complex correlation between emission and air pollution. We further updated the emission inventory based on backpropagating the gradient of the loss function measuring the deviation between NN-CTM and observations from surface monitors. We first mimicked the CTM model with neural networks (NN) and achieved a relatively good representation of CTM with similarity reaching 95 %. To reduce the gap between CTM and observations, the NN model would suggest an updated emission of NOx, NH3, SO2, VOC and primary PM2.5 which changes by −1.34 %, −2.65 %, −11.66 %, −19.19 % and 3.51 %, respectively, on average of China. Such ratios of NOx and PM2.5 are even higher (~10 %) particularly in Northwest China where suffers from large uncertainties in original emissions. The updated emission inventory can improve model performance and make it closer to observations. The mean absolute error for NO2, SO2, O3 and PM2.5 concentrations are reduced significantly by about 10 %~20 %, indicating the high feasibility of NN-CTM in terms of significantly improving both the accuracy of emission inventory as well as the performance of air quality model.

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Mimicking atmospheric photochemical modeling with a deep neural network

Xing

Zheng

et al. 2022

Atmospheric Research

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Data Assimilation of Ambient Concentrations of Multiple Air Pollutants Using an Emission-Concentration Response Modeling Framework

Cited by 12 publications

References 31 publications

Exploring deep learning for air pollutant emission estimation

Exploring deep learning for air pollutant emission estimation

Exploring Deep Learning for Air Pollutant Emission Estimation

Mimicking atmospheric photochemical modeling with a deep neural network

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